Structural light three-dimensional vision measurement is widely used in industrial measurement and other fields because of the advantages of non-contact, fast speed and moderate accuracy. The calibration accuracy of a structured light vision sensor determines the final detection accuracy level of structural light three-dimensional vision measurement. The line structured light vision sensor calibration process includes two aspects of cameral internal parameter calibration and light plane parameter calibration. The calibration process mainly uses the internal parameters of the camera and other auxiliary tools to determine the plane equation of the light plane in the camera coordinate system.
Commonly-used calibration methods of structured light sensors mainly include a free moving target method and a mechanical motion adjustment method. The free moving target method usually uses a one-dimensional target, a two-dimensional planar target, or a three-dimensional target to complete calibration of optical plane parameters. This method features easy target processing and high calibration accuracy and efficiency and is thus widely used. The mechanical motion adjustment method usually uses mechanical motion platform with an encoder, mechanical arms and other devices to calibrate structured light sensors. This method has many processes requiring manual adjustment, and the accuracy thereof mainly depends on the accuracy of the mechanical motion platform.
Chinese Patent Application No. CN200810239083.4 discloses a method for calibrating structured light parameters based on a one-dimensional target. The method uses at least three feature points with known spatial constraints of a one-dimensional target, and is combined with a perspective projection equation, to calculate spatial three-dimensional coordinates of the feature points in a camera system coordinate system according to the length constraints and direction constraints of the feature points and fit the spatial three-dimensional coordinates to obtain the light plane equation. This method requires high processing accuracy for one-dimensional targets and is therefore sensitive to image noise.
Chinese Patent Application No. CN200710121397.X discloses a calibration method for structural parameters of a structured light vision sensor. The method uses a plane target with multiple nonlinear feature points, obtains the center of a light stripe and coordinates of nonlinear feature points on a target image by moving a position of a plane target for many times, calculates the three-dimensional coordinates of the center point of the light stripe in the camera coordinate system by homography matrix, and fits the light plane equation according to the three-dimensional coordinates. This method is widely used because of its high calibration efficiency and high precision. However, this method cannot extract high-precision feature points while extracting high-quality light stripe images.
Chinese Patent Application No. CN201510307016.1 discloses a calibration method of a line structure light vision sensor based on a parallel double cylindrical target. The method uses a freely moving parallel double cylindrical target, places the target at least once in a suitable position, to extract the center of the light stripe image and to fit the elliptical image of the light stripe in the image. This method establishes an equation between two spatial ellipses and their corresponding images based on perspective projection transformation, solves the light plane equation by constraints that the elliptical short axis is the same as the diameter of the cylinder. This method requires a high-precision three-dimensional calibration target and high processing cost, and it is difficult to obtain a high-quality calibration image due to factors such as reflection and occlusion.
From the above analysis, it can be seen that existing structural light parameter calibration methods require high-precision targets with feature points or high-precision geometrically constrained targets. However, due to the current level of material processing technology, it is difficult to achieve the positional accuracy or geometric constraint accuracy of the feature points to the micron level while ensuring the image quality of the light stripe, and it may cause deviation if the transformation matrix is calculated by using an image homography matrix method. When a laser stripe is projected on metal or a target such as a ceramic target, the extraction accuracy of the center of the stripe may be deteriorated due to strong reflection or diffuse reflection.
It would be desirable to improve the devices and methods for structural light parameter calibration to overcome these and other deficiencies of conventional designs.